Data scientists wait days for GPUs.
It should take minutes.
GPU VMs and managed Kubernetes clusters — pre-validated, production-ready, across five providers. Self-service within guardrails. No driver debugging. No ticket queues.
GPU budgets are tripling.
Where is the money going?
GPU spend attributed per team, project, and provider — in one dashboard. Utilization metrics show whether expensive GPUs are working or idle. Cost visibility before the bill arrives.
Five GPU clouds. Five consoles.
One platform engineering team.
Each provider has its own provisioning API, networking model, and compliance surface. emma consolidates all of it into a single operational layer.
GPU infrastructure is running outside your governance perimeter.
Fix that.
RBAC, tagging, cost attribution, and audit trails — applied to GPU workloads the same way they're applied to everything else. Governed inference templates. No shadow AI.
See it running. 45 minutes.
GPU provisioning, monitoring, networking, and governed inference — live. No slides.
GPU VMs and managed K8s clusters with pre-validated images — self-service, governed, across five providers.
GPU VMs across five providers
Select GPU type, provider, region, and image in the emma VM wizard or via API. Same flow across AWS, GCP, Azure, emma, and Nebius.
Pre-validated NVIDIA images
VMs launch with driver-optimized ML/DL images. Working environment from first boot — not a day of driver debugging.
GPU K8s in under 5 minutes
Fully managed Kubernetes with GPU node pools across AWS, Azure, and GCP. Pre-validated CUDA. No cluster ops.
86% of enterprises expect AI infrastructure budgets to more than triple. emma gives you attribution and utilization data so the money goes where it should.
Unified cost dashboard
GPU spend attributed per team, project, and provider in one view. No manual reconciliation across cloud billing consoles.
Utilization as evidence
GPU utilization, vRAM, and memory metrics show whether expensive GPUs are working or idle. Evidence for right-sizing.
Cost preview before deployment
Inference workflow templates include cost preview. Every deployment attributable. See costs before the workload runs.
Each GPU cloud has its own API, networking model, and compliance surface. emma consolidates provisioning, connectivity, and governance into a single layer.
Single control plane
Provision GPU VMs and managed K8s across AWS, GCP, Azure, emma, and Nebius from one interface. Same workflow regardless of provider.
High-speed cross-cloud connectivity
GPU workloads connected across providers through emma's private networking backbone. Training on one provider, inference on another — low latency, no manual config.
Reduce egress cost
Data transfer via emma's backbone instead of public internet routing. The hidden networking tax of multi-cloud AI architectures — eliminated.
GPU infrastructure shouldn't operate outside your compliance perimeter. emma applies the same RBAC, tagging, and audit standards to GPU that it applies to everything else.
Same RBAC, same policies
GPU VMs and K8s clusters inherit your existing governance model. Tagging enforced at provisioning. No separate compliance surface.
Governed inference templates
Reusable, versioned deployment templates with guardrails — instance limits, parameter constraints, RBAC. Self-serve within policy.
Full audit trail
Every GPU VM lifecycle event, every cluster provision, every inference deployment — auditable. Governance by design.
One platform. Five operational layers.
emma is not a cloud provider. We don't own your infrastructure, lock you in, or compete with your cloud vendors. We operate across them.